{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to add the package to the Python path to import and use it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys, os.path\n",
    "sys.path.append(os.path.expanduser(\"~/Repositories/erds\"))  # adjust this path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we import the `Erds` class and `loadmat` to load data stored in a .mat file. We will also need `numpy` later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.io import loadmat\n",
    "from erds import Erds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we load the example data set and store the EEG signals in `X` and the associated labels in `y`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = loadmat(\"test.mat\")\n",
    "X = data[\"X\"]\n",
    "y = data[\"y\"].squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3, 4096)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data comprises 100 epochs with 3 channels (Laplacians at C3, Cz, and C4) and 4096 samples. The sampling rate is 512 Hz, so each epoch is 8 s long."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1], dtype=uint8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each epoch is associated with one out of two labels. The data was recorded during a motor imagery task, where the subject imagined right hand movement (corresponding to a label of 0) and left hand movement (corresponding to a label of 1), respectively.\n",
    "\n",
    "Since the data is already in the correct format (epochs, channels, samples), we are ready to compute ERDS maps. After creating an `Erds` object, we call its `fit` method and supply data from class 0. We also specifiy the sampling frequency. Otherwise, we're happy with the default values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Erds object>\n",
       "  n_times: 128\n",
       "  n_freqs: 513\n",
       "  baseline: whole epoch\n",
       "  fs: 512 Hz\n",
       "  Input data:\n",
       "    epochs: 50\n",
       "    channels: 3\n",
       "    length: 4096 samples\n",
       "  ERDS data:\n",
       "    frequency bins: 513\n",
       "    channels: 3\n",
       "    length: 128 samples"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "maps = Erds()\n",
    "maps.fit(X[y == 0, :, :], fs=512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After calculating the maps, we are presented with some basic features of our `Erds` object. For example, we see that each ERDS map consists of 513 frequency bins (which are always computed from 0 Hz up to the Nyquist frequency, which is 256 Hz in our example) and 128 time points (equally spaced across the whole epoch from 0 s to 8 s).\n",
    "\n",
    "Our baseline for the ERDS values is the whole epoch since we didn't explicitly specify a baseline.\n",
    "\n",
    "Finally, we get a quick overview of the shapes of the input EEG data (50, 3, 4096) and the resulting ERDS maps data (513, 3, 128).\n",
    "\n",
    "We are now ready to plot the maps by simply calling the `plot` method. Again, we do not specify any arguments and therefore use the defaults."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1hf2HGw59m/a3fF4W89EQ2jK2Zu3fT2sdravtFNa+TNJxoLd1UuQS6PPCecri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\nETd9UGov3+dWrmNzd7m+71UU0V60lqbjOtq6SjW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w94PFKKt/l5MiE37L3ufavMf6sTdp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MKvQtFlqEWlUCgUil2N7lhUQcMvSCOBmy3LGfIiic5akASSbLB2GnVy1i1jTWGmnlUHKpbRacc2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\nAOrPPTo3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f2519e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "maps.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
